Keyword clustering is the step between a long keyword list and a content plan. Done well, it tells you how many pages to write and which terms each one should target. Done badly, it produces either a pile of thin pages competing with each other or one bloated page trying to answer questions that need different answers. The difference almost always comes down to how the groups were made.
What keyword clustering is
Semrush’s guide, keyword clustering by Carlos Silva, published in October 2025, defines it as “an SEO technique centered on grouping search terms that share the same search intent.” The aim is one page per intent, with every keyword that shares that intent pointed at it.
The payoff is that a single well-built page can rank for far more than its headline term. Semrush gives the example of one page ranking for about 2,200 keywords, with an estimated 183,100 monthly visits from the US. That is Semrush’s example, measured with its own estimates, but the pattern is familiar: strong pages win whole families of queries, not single phrases.
Why shared words are the wrong signal
The received wisdom is to cluster lexically: put every keyword containing “running shoes” in one group, every keyword containing “trail” in another. It is quick, a spreadsheet can do it, and it is often wrong. Words describe the query. They do not tell you what the searcher wants.
Two phrases can differ by one word and want completely different pages: one a buying guide, the other a definition. Two phrases with no word in common can want the same page, because they are different ways of asking one question. Grouping by words merges the first pair and splits the second, which is exactly backwards.
Semrush’s guide points to the better signal: whether “the same pages rank well for those keywords.” If the results for two queries are largely the same set of URLs, the search engine is treating them as one need, and one page can serve both. If the results barely overlap, they need separate pages, however similar they look. Our guide to search intent covers how to read what a results page is telling you.
| Approach | Groups keywords by | Main risk |
|---|---|---|
| Lexical clustering | Shared words or stems | Merges different intents; splits identical ones |
| Intent labelling | Your judgement of what the searcher wants | Guesswork on ambiguous terms |
| SERP similarity | Whether the same pages rank for both terms | Takes longer; results shift over time |
How to create keyword clusters
Semrush lays out five steps. They are worth following in order, because each depends on the one before.
- Build a keyword list. Gather every term relevant to the topic, including the long-tail variants your main tool surfaces. Our post on long-tail keywords covers where those come from.
- Categorise by intent. Label each term informational, commercial, transactional or navigational, then group terms whose results share the same ranking pages.
- Plan by priority. Weigh each cluster by your resources, its combined search volume, its difficulty and how well it serves your goals.
- Optimise or create content. Give each cluster one page, either an existing page improved or a new one written for it.
- Track rankings by cluster. Watch the group, not just the headline term.
Step two is where the SERP check does its work. Start with the term in each group that has the most volume, look at the pages ranking for it, then check the other candidates against that set. Terms whose results share several of the same ranking URLs stay. Terms whose results look different move to another cluster or start their own. Set your threshold for “several” once and apply it consistently, because a vague rule produces vague clusters.
Step three is where a long list becomes a short plan. Combined volume tells you how much demand a cluster holds, but it is not the only weight. A cluster with modest volume that sits close to what you sell can be worth more than a large one you can only serve with general information. Difficulty matters too: a cluster dominated by pages from far stronger sites may be better left until your own pages on neighbouring topics are ranking. Work down the list in the order that gives you the most achievable wins first.
Keyword cluster analysis before you write
Before turning clusters into briefs, compare them with what you already have. A cluster that maps to an existing page is an optimisation job, not a new article. A cluster that maps to two of your existing pages is a warning: you already have pages splitting one intent, the problem our post on keyword cannibalisation describes. Merge those before writing anything new.
Then look at how the clusters relate to each other. Several narrow clusters often sit under one broad topic, and that structure maps directly onto a pillar page with supporting articles. Our guide to pillar pages covers how to build the hub and link the cluster pages back to it. The clustering exercise has done most of the planning already: each cluster is a supporting page, and the broad topic they share is the pillar.
Track by cluster, not by keyword
The final step is the one most teams skip. Once a page targets a cluster, its success is the cluster’s success. Watching one headline keyword can make a page look stagnant while it is steadily winning dozens of related queries, or make it look healthy while the wider group slips away. Group your rank tracking the same way you grouped your keywords.
Revisit the clusters themselves now and then. Search results change: a term that once shared results with your main page can drift towards a different kind of page, and when it does, the cluster should split. A quarterly look at your most valuable clusters is usually enough to catch that before the rankings show it.
The limitation is that SERP similarity is a snapshot. The results you compare today are for one location and one moment, and personalisation, freshness and new result types can all shift them. Similar results are strong evidence that two terms share an intent, not proof. Use them to make the grouping decision, then let ranking data over the following months confirm or correct it.
It also helps to keep the reasoning visible. Record, next to each cluster, the main term and the ranking pages you compared it against. When a cluster later underperforms, you can check whether the results have moved since you grouped it, rather than guessing whether the content or the grouping was at fault.
Frequently asked questions
What is keyword clustering?
Grouping search terms that share the same search intent so that one page can target the whole group. Semrush defines it in those terms.
How do I know if two keywords belong in the same cluster?
Check whether the same pages rank well for both. Largely overlapping results suggest one page can serve both; different results suggest separate pages.
Can I cluster keywords by shared words?
You can, but it is unreliable. Similar phrases can need different pages, and different phrases can share one. Use the results pages as the deciding signal.
How does keyword clustering relate to cannibalisation?
Clustering prevents it. When one intent has one page, your own pages do not compete for the same queries.
The takeaway Cluster by the pages that rank, not the words in the query. One page per intent, checked against existing content, tracked as a group and revisited when the results change.
